James H. Faghmous, Ph.D.
Assistant Professor & Founding CTO
The Arnhold Institute for Global Health
Icahn School of Medicine at Mount Sinai, NYC
Event Information and Registration
“Machine Learning for the Triple Aim: Advances, Challenges, and Opportunities”
Presenter: Jonathan Chen, MD, PhD
Assistant Professor of Biomedical Informatics
Stanford University
Jonathan H Chen MD, PhD is a physician-scientist with professional software development experience and graduate training in computer science. He continues to practice Internal Medicine for the concrete rewards of caring for real people and to inspire his research focused on mining clinical data sources to inform medical decision making.
Chen co-founded a company to translate his Computer Science graduate work into an expert system to solve organic chemistry problems, with applications from drug discovery to a practical education tool distributed to students across the world. To gain first-hand perspective in tackling the greater societal problems in health care, he completed medical training in Internal Medicine and a VA Research Fellowship in Medical Informatics. He has published influential work in venues including the New England Journal of Medicine, JAMA, JAMA Internal Medicine, Bioinformatics, Journal of Chemical Information and Modeling, and the Journal of the American Medical Informatics Association, with research awards and recognition from the NIH Big Data 2 Knowledge initiative, National Library of Medicine, American Medical Informatics Association, Yearbook of Medical Informatics, and American College of Physicians, among others.
In the face of ever escalating complexity in medicine, integrating informatics solutions is the only credible approach to systematically address challenges in healthcare. Tapping into real-world clinical data streams like electronic medical records with machine learning and data analytics will reveal the community’s latent knowledge in a reproducible form. Delivering this back to clinicians, patients, and healthcare systems as clinical decision support will uniquely close the loop on a continuously learning health system. Dr. Chen’s group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to medicine that will deliver better care than what either can do alone.
Refer to Dr. Chen’s web-page for additional in-depth bio information, publication lists, CV, etc.
http://web.stanford.edu/~jonc101
Join us at the 11th XLDB conference this year at Stanford University to hear from experts at the intersection of Machine Learning, IoT and Healthcare. We have two and a half days of discussions, with participation from academia, government and industry.
Agenda: https://conf.slac.stanford.edu/xldb2018/agenda
Registration: https://app.certain.com/profile/form/index.cfm?PKformID=0x2700338abcd
Here is a sample of topics that will be presented:
Large-scale inference across genomes and health records with the Global Biobank Engine, A data biosphere for all of us (and All of Us), Big data in the Intensive Care Unit, Data Infrastructure for the DAWN of Widespread ML, Is Machine Learning just another workload for Data Systems?, New Frontiers in Medical Imaging in the Age of Big Data and Precision Medicine,Predictive and Personalized Virtual Care using IoMTs, From big data to real policy: Making EHR data matter in health care,IoT Makes Big Data Look Small
Jason Fries, PhD,
Research Scientist,
BMIR, Stanford University
ABSTRACT:
In biomedicine, obtaining expert-labeled training data is a key bottleneck to using machine learning methods. However, recent efforts such as Stanford’s Snorkel system are creating new ways of using expert heuristics to train large-scale machine learning models. This approach provides many practical benefits, from improving classification performance by modeling the unobserved accuracies of label sources, to creating software artifacts that can be shared, modified, and applied to new datasets. We outline two successful applications of Snorkel in biomedicine: (1) analyzing patient clinical notes to extract implant-related complications following total hip replacement; and (2) identifying patients with rare cardiac malformations using MRI video data from the UK Biobank.
Jason Fries, PhD:
Jason Fries is a research computer scientist at the Stanford Center for Biomedical Informatics Research working with Prof. Nigam Shah. He recently completed a postdoctoral fellowship with Prof. Chris Ré and Scott Delp as part of Stanford’s Mobilize Center. His research interests include methods for training machine learning models using limited hand-labeled data, such as weak supervision and few-shot learning, with a focus on extracting information from unstructured biomedical data.
Brandon Fornwalt MD, PhD
Investigator II, Associate Professor
Geisinger Health System
Abstract:
The overall goal of our group is to leverage data-driven approaches to help improve patient outcomes. This talk will demonstrate examples of how we can work towards this goal by leveraging large clinical datasets, data science and machine learning. Specific examples include: 1) using 46,583 clinically-acquired 3D computed tomography images of the brain to develop and implement a deep learning model to efficiently reprioritize radiology worklists for quicker diagnosis of intracranial hemorrhage; 2) using deep learning to analyze 723,754 echocardiographic videos of the heart to accurately predict patient survival; 3) analyzing 2 million 12-lead electrocardiographic tracings from the heart to predict clinically relevant future events and 4) optimizing evidence-based care delivery for a population of >10,000 patients with heart failure using machine learning.
Bio:
Dr. Fornwalt attended the University of South Carolina as an undergraduate in mathematics and marine science. He then worked in a free medical clinic for a year before starting an MD/PhD program at Emory and Georgia Tech. After finishing his degrees in 2010, he completed an internship in pediatrics at Boston Children’s Hospital before becoming an Assistant Professor at the University of Kentucky. After four years on faculty in Kentucky, Dr. Fornwalt moved to Geisinger where he founded Geisinger’s Department of Imaging Science and Innovation and focuses on data-driven approaches to improving patient outcomes. Dr. Fornwalt is also a radiologist and a member of the Geisinger Heart Institute.
In the wake of the COVID-19 pandemic, several support structures for children and their families have been disrupted. Underserved communities who were already disadvantaged prior to the pandemic now face further setbacks with regards to accessing digital resources, mental health services, food and housing support, and remote learning. On the frontlines of this battle are our educators who are grappling with the complexities of how to support their students during these uncertain times.
The Stanford Center for Population Health Sciences in partnership with Born in Bradford and the Centre for Applied Education Research cordially invite you to join a virtual conference where we will learn how researchers in the US and UK are tackling these issues.
In the wake of the COVID-19 pandemic, several support structures for children and their families have been disrupted. Underserved communities who were already disadvantaged prior to the pandemic now face further setbacks with regards to accessing digital resources, mental health services, food and housing support, and remote learning. On the frontlines of this battle are our educators who are grappling with the complexities of how to support their students during these uncertain times.
The Stanford Center for Population Health Sciences in partnership with Born in Bradford and the Centre for Applied Education Research cordially invite you to join a virtual conference where we will learn how researchers in the US and UK are tackling these issues.